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Supervised Machine Learning

In: Multivariate Statistics and Machine Learning in R For Beginners

Author

Listed:
  • Andreas Tilevik

    (University of Skövde)

Abstract

This chapter introduces a variety of supervised machine learning models for classification. It begins by illustrating how to validate machine learning models using methods, such as the hold-out method and cross-validation, as well as how to prepare data for hyperparameter tuning. Next, this chapter illustrates how to implement supervised learning in R using linear discriminant analysis, logistic regression, decision trees, random forests, k-nearest neighbors, and Gaussian naive Bayes. This chapter ends with a section on how to compare machine learning models with repeated cross-validation and how to deal with imbalanced datasets by using the method of random oversampling examples.

Suggested Citation

  • Andreas Tilevik, 2025. "Supervised Machine Learning," Springer Books, in: Multivariate Statistics and Machine Learning in R For Beginners, chapter 0, pages 171-251, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-01851-9_11
    DOI: 10.1007/978-3-032-01851-9_11
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